A topic-based sentence representation for extractive text summarization
Nikolaos Gialitsis, Nikiforos Pittaras, Panagiotis Stamatopoulos
Abstract
In this study, we examine the effect of probabilistic topic model-based word representations, on sentence-based extractive summarization. We formulate the task of summary extraction as a binary classification problem, and we test a variety of machine learning algorithms, exploring a range of different settings. An wide experimental evaluation on the MultiLing 2015 MSS dataset illustrates that topic-based representations can prove beneficial to the extractive summarization process in terms of F1, ROUGE-L and ROUGE-W scores, compared to a TF-IDF baseline, with QDA-based analysis providing the best results.- Anthology ID:
- W19-8905
- Volume:
- Proceedings of the Workshop MultiLing 2019: Summarization Across Languages, Genres and Sources
- Month:
- September
- Year:
- 2019
- Address:
- Varna, Bulgaria
- Editor:
- George Giannakopoulos
- Venue:
- RANLP
- SIG:
- Publisher:
- INCOMA Ltd.
- Note:
- Pages:
- 26–34
- Language:
- URL:
- https://aclanthology.org/W19-8905
- DOI:
- 10.26615/978-954-452-058-8_005
- Cite (ACL):
- Nikolaos Gialitsis, Nikiforos Pittaras, and Panagiotis Stamatopoulos. 2019. A topic-based sentence representation for extractive text summarization. In Proceedings of the Workshop MultiLing 2019: Summarization Across Languages, Genres and Sources, pages 26–34, Varna, Bulgaria. INCOMA Ltd..
- Cite (Informal):
- A topic-based sentence representation for extractive text summarization (Gialitsis et al., RANLP 2019)
- PDF:
- https://preview.aclanthology.org/naacl24-info/W19-8905.pdf